Understanding allergies

The identification of new biomarkers in the lab is already having a significant impact on research into allergens at a clinical level, says Carl-Johan Ivarsson. But can computer software help scientists to progress these findings from bench to bedside, whilst reducing the need for animal testing at the same time?

In 2005, a large EU-funded research project was launched to develop and optimise in vitro test strategies that could reduce or replace animal testing for sensitisation studies. By using a multi-disciplinary approach, this study is helping to address skin and lung sensitisation by focusing on the impact of compounds on cellular-molecular interactions, which play a central role in the development and elicitation of many allergies.

Because there are not yet any in vitro tests or test strategies available to test chemical compounds on their potential to induce allergies, the aim is to develop alternatives to animal tests currently used for the risk assessment of potential skin or lung sensitisers.

The project, known as Sens-it-iv (http://www.sens-it-iv.eu/), combines both private and public research institutions, plus several industrial and societal interest organisations. A key partner involved with the project is the European Centre for the Validation of Alternative Methods (ECVAM) at the Joint Research Centre. The presence of ECVAM ensures a clear focus on tests and testing strategies that can be validated, which is a prerequisite for eventual regulatory acceptance.

Dr Ann-Sofie Albrekt from Lund University is currently working in this exciting area. Based in the Department of Immunotechnology – headed by Professor Carl Borrebaeck, a sub-coordinator of Sens-it-iv – she is currently focusing on two key areas: looking for new biomarkers in cancer studies, and performing important research on allergens.

"Worldwide, more and more people are suffering from allergies, which means that this area has become an important health concern," says Dr Albrekt. "As a scientist, I am interested to find out why otherwise harmless compounds can often elicit an adverse immune response in humans."

Dr Albrekt is using Qlucore Omics Explorer, sophisticated data analysis software, to help her to get the most out of the data being produced by this research.

"Although gene expression studies are proving invaluable to the study of allergens, the amount of data that is produced by these experiments is enormous," she says. "It is impossible to derive any real biological meaning from these findings unless sophisticated data algorithms are used to help interpret this data effectively."

For this reason, most of the software designed for use in this area has mainly focused on the ability to handle increasingly vast amounts of data – which means that the role of the scientist/researcher has been largely set aside. As a result, a lot of data analysis has been passed on to bioinformaticians and biostatisticians. In most cases this has several drawbacks, since it is typically the scientists themselves who know most about biology.

"Some data analysis applications can be very complicated and difficult to use, even for specialist statisticians, so it is very important to find software that has been developed by scientists, for scientists," Dr Albrekt says. "I am not a statistician, but I found Qlucore very easy to use, and without the need for any manuals or training. We started using the software straight away, and the fact that it is highly intuitive means that we were actually able to learn by using it."

Sophisticated bioinformatics software enables scientists to analyse very large data sets by a combination of statistical methods and visualisation techniques such as Heatmaps and Principal Component Analysis (PCA). With the benefit of instant user feedback on all actions, as well as an intuitive user interface that can present all data in 3D, scientists studying allergens and other aspects of human biology can now easily analyse their data in real-time, directly on their computer screen.

Modern data analysis software now enables researchers to use this approach with extremely large data sets – even those with more than 100 million data points – on a regular PC. This specialist software can even take advantage of annotations and other links connected with data being studied, as well as a number statistical functions such as false discovery rates (FDR) and p-values.

As such, the research being conducted at the Department of Immunotechnology represents a significant breakthrough in how modern data analysis is being performed. Less than 10 years ago, researchers were only able to work with analysis methods that provided information about single genes. The number of information points per subject has grown to hundreds of thousands in recent years, thanks to important technological advances in this area.  

Most recently, the overall performance of data analysis software has been optimised significantly. According to Dr Albrekt, modern data analysis software can be used to transform high dimensional data down to lower dimensions, which can then be plotted in 3D on a computer screen and rotated manually or automatically, so that they can be examined by the naked eye.  

These instant visualisation techniques are combined with powerful statistical methods and filters, all of which are handled with only one mouse-click. Different colours can make this analysis even easier, as each sub-group can be labelled with its own unique colour.  

As such, the view of data can be changed in an instant, so researchers are only looking at the specific sub-group that interests them at any given moment. As a result, it is very easy to add and/or remove data as necessary, without having to start from the beginning and re-analyse the entire data set.

"When you are looking at such a large amount of genetic data, there is bound to be a number of confounding factors that distort the data," says Dr Albrekt.  "The ability to remove this "noise" is very important, in order for researchers to be sure that they are working with the most reliable data. Advanced data analysis software like Qlucore Omics Explorer makes it much easier to make a qualified judgment about the amount of noise present, so that researchers can see true patterns as they emerge."  

In fact, with key actions and plots now displayed within a fraction of a second, scientists can increasingly perform the research they want and find the results they need instantly. This approach has helped open up new ways of working and, as a consequence, has helped to bring the biologists back into the analysis phase.

When performing her own research, Dr Albrekt typically begins her workflow by coding any interesting factors (and confounding factors) into a single file. She then imports the data and looks at the pattern of samples in order to search for both anticipated and non-anticipated sub-patterns. At this point, she begins to examine the sub-patterns using the coded factors that she had identified earlier – for example, by using the application's colour function and/or eliminating the factor function. She can then look for any significant differences by using statistical tests.

"We can test the robustness of these findings by using kNN visualisation, randomisation and permutation tools," Dr Albrekt explains. "That way, we can make a decision on which variables to trust, and then annotate any significant variables that we have found and export them for functional analysis using another software tool."

With the freedom, speed and flexibility provided by this approach, it is now possible to evaluate and test a number of different scenarios and hypothesis in a very short time, and to fully understand the data being examined. This technique makes it possible for researchers to combine very large amounts of data, and therefore to conduct analysis in ways that were simply not possible before.

"In our studies, we are dealing with very large amounts of data, sometimes between 10 and 100 million data points, which we tend to view as graphics. With other software, these graphics would take a long time to appear, but with the latest data analysis tools, the information is presented instantly," Dr Albrekt says. "As a result, we can be much more creative with our theories, as we can easily test any number of hypotheses in rapid succession."

Although Dr Albrekt is currently using data analysis software to study gene expression micro array data, other researchers have used it to study protein array data, miRNA data, and RT-PCR data as part of their research studies. It has also been used to analyse protein data from 2-D gels, image analysis data, and with any data set of multivariate data of sizes up to 1000 samples and 100,000 variables, or 1000 variables and 100,000 samples.

The latest technological advances in this area are making it much easier for researchers to compare the vast quantity of genomic data generated, to test different hypotheses, and to explore alternative scenarios within seconds. Not only that, but the latest generation of data analysis software is also helping scientists to regain control of analysis and to realise the true potential of gene expression profiling.

According to Dr Albrekt, her own research efforts will continue to focus on both the Sens-it-iv allergen studies, as well as on the ongoing cancer research within CREATE Health, which is a strategic centre for translational cancer research.

"In terms of the work we are doing to support Sen-it-iv, I feel confident that a successful project outcome will contribute to the reduction in the number of animals required for safety testing and the establishment of more accurate tools for product development," she says. "This project will therefore be of substantial benefit to all European citizens, and that goal continues to motivate me to make new discoveries in this area."